Cross-country policy comparison of 30 km/h speed limits
Bibliographic record
Abstract
30 km/h speed zones are one of the most cost-effective road safety interventions to enhance the safety and liveability of local streets. However, only two zones are currently implemented in the state of Victoria, Australia, and these zones are not widely adopted across Australia. Greater understanding of the barriers to implementation is needed to rapidly advance implementation of this effective road safety intervention. We aimed to identify and explore barriers and enablers of implementation of 30 km/h speed limits in the state of Victoria, Australia, and compare this to implementation in an area where 30 km/h speed zones have been implemented successfully – British Columbia, Canada. We conducted 26 semi-structured interviews with relevant policy partners. Data were analysed abductively through reflexive thematic analysis. Six key themes were identified: (i) Appetite for change; (ii) Policy misalignment; (iii) Lack of local evidence; (iv) Council capacity; (v) Need for ‘self-explaining’ roads, and (vi) Equity lessons in implementation. We demonstrated momentum and support for 30 km/h speed zones. However, state government policy reform is needed to enable easier implementation by local councils. This study provides critical insights into the complexity of implementing road safety interventions and opportunities to enhance systems to catalyse change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".